Yes, You Can Change AI’s Opinion. Here’s How.
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Robert Maynard, Jr. 11 hours ago ⋅ 12 min read Robert Maynard, Jr. Founder at AnswerShare Bio Follow 65 READS Editorial Reviewed: Jennifer McDonald Editorial Reviewed This post was sponsored by AnswerShare. The opinions expressed in this article are the sponsor’s own. You’ve been working hard on AI visibility. Your company is finally showing up when prospective customers ask questions about your market. You earned the mention. You’re in the conversation. Then someone asks a more direct question: “Do you recommend them?” And if LLMs are your critics, can you help them understand your brand better? The frontier models have been told by their makers to avoid recommendation risk, especially in high-stakes or YMYL (Your Money or Your Life…but you knew that) instances. What we’ve seen is that the models are doing this by surfacing virtually every complaint or poor review they can find. If any negative reviews exist, no matter how comparatively minimal or unwarranted, they often issue a caution or warning flag. Consider this: What we have seen is an over-correction. In its attempt to defer risk, AI models often bring up virtually every negative review or historical complaint they can find, issue a caution and recommend competitors. If they do that for your brand, you are now out of the conversation because neither the AI nor the user understands the context. All that hard AI visibility work, just to send prospects the other way. We wanted to know one thing: if AI systems had the full context behind a company’s complaints, meaning how many customers it serves, how long it’s operated, and how it responded, would they still warn buyers away? For one of our clients, we saw significant impact in three days. We ran prompts to the frontier AI systems about the client’s reputation. We sent the prompts through the API with the same neutral questions and no reference to previous conversations or leading questions, so that sycophancy was not an issue. In our reputation checks, the AI responses surfaced a small set of public complaints, used them to frame the business as a potentially risky choice, and named preferred competitors. In this client’s case, there were more than 75 positive reviews, 5 negative reviews and two BBB complaints. Against 20 customers, 7 complaints is a red flag. Against 35,000 customers over 13 years, seven complaints imply a customer satisfaction rate most businesses would publish (99.98%). We published llms-full.txt files with the full brand story. Rather than waiting for a crawl that may never come, as some research suggests, we also put that content on a “worker” (more on that below) at the CDN edge. That’s where we tell “the rest of the story,” as Paul Harvey famously called it. After only three days, we began seeing balanced answers. After 14, we saw 100% recommendations. The table below are the results after 14 days. The complaints remained part of the discussion, but responses began acknowledging the company’s operating history and scale alongside them. We also saw recommendations suggesting the user includes the company among the options a prospective customer should evaluate. AI systems can overweigh a few complaints when they lack the denominator, which includes: So, we can surmise that the AI had the numerator. It didn’t have the denominator. Instead, present the brand’s entire story and directly address the negative information already in the corpus. The AI system retains its judgment, but it now has enough data to weigh how the facts affect the final recommendation. “You can’t put lipstick on a pig,” as my grandfather would say. So, if the brand story can’t be supported and the brand has rightfully earned a poor rating, nothing will change that. But, if your brand truly does have a good reputation and great ratings answers should reflect that based on the broader corpus. The question we solved is: “But how? For these experiments, we use code to route the appropriate traffic to the machine layer (AI crawlers) or the human layer (users, Googlebot, and other search bots). That code is called a “worker.” Our big “AHA” moment was when we learned that we can put content on that worker that AI will ingest and infer from, in addition to the routing code. Our client’s llms files were crawled a total of 154 times in two months, while the worker was crawled 744,566 times. Repetition works as evidenced by a recent peer-reviewed paper. (Atharv Naphade, “Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering,” arXiv:2601.06189, 8 January 2026.) It works on humans. Why else do you see the same mobile provider ad 6 times during the Super Bowl? Our experience tells us it works on AI as well. Once I noticed that these repeated worker crawls operated like billboards for AI, I decided to call the workers “billboards.” (I know, uber inspired) This is where the brand presents its case, supported by facts and sources. Think of it as a homepage for the AI. And we tell that story when they visit. Every. Single. Time. For some sites we run, that can be millions of times per month. The files included in our “billboards” addressed the complaints head on, their dates, direct links, scope and the company’s account of its response. For instance, they had two BBB complaints, but they were about sales tactics from a single employee; nothing to do with customer outcomes. We explained the situation within the billboard and added that the employee was counseled, and management increased controls so it wouldn’t happen again. Responses either dropped the mention of the BBB complaints or positioned them as not indicative of their delivered performance. Our llms-full.txt file is published openly on the public, human web layer—accessible to any standard browser, GPTBot, and Googlebot alike. The CDN worker simply ensures this exact, byte-for-byte content is delivered cleanly to machine crawlers at the edge. Because the full context is publicly accessible to everyone, nothing is hidden, concealed, or manipulated. Image created by AnswerShare, September 2026.It’s just presented differently based on the audience; no different from responsive design reformatting for mobile or markdown conversion. Now the brand has presented its entire story and addressed negative information that is in the corpus directly. The AI system retains its judgment but now has enough data to weigh how the facts it has affect the final recommendation.
Source: Search Engine Journal
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